快速分形图象压缩算法的研究

分形编码是一种非常有潜力的图象压缩技术，但因其与具很高的时间复杂度，故至今未能获得广泛的应用，本文提出了旨在降低分形编码复杂度，缩短编码时间的分形图像压缩改进算法，该算法采取递归四树分块结构，将多种块分类技术相结合，并通过预计算，旋转与翻转标准化等方法降低计算复杂度，采取高效的存储方案来提高压缩化，力求在图像质量，压缩比和编码时间上取得了良好的折Ｚｏｎｇ使分形编码更加实用化。实验结果表明，采用这种     

分形图像压缩

欧氏几何学不能处理自然界中非常复杂的形状，这只能借助于分形几何学，分形图像压缩就是利用分形几何学的有关原理进行编码，达到图像压缩之目的。     

基于小波分析的分形图像去噪压缩方法

近十几年来,分形(Fractal)在图像压缩技术中的应用已成为图像数据压缩领域中最为热点的问题之一。其压缩比在理论上可超过经典压缩方法的几个数量级。但实际上,原始图像经常被噪声污染。噪声的存在一方面使得图像编码的时间延长,另一方面降低了图像的信噪比,使图像质量明显下降。本文对小波变换、分形压缩编码的原理和特点进行了分析;结合小波变换和分形压缩编码,利用分形的自相似性,研究了基于小波域的分形图像去噪、压缩方法。该方法有效地减少了计算复杂度和编码时间并获得了良好的图像质量。实验结果表明,该方法在较大的压缩范围内,能够获得好的压缩结果,同时也表明采用这种方法的潜力之所在。     

Strkuctured-light Image Compression Based on Fractal Theory

The method of fractal image compression is introduced which is applied to compress the line structured-light image.Based on the self-similarity of the structured-light image, we attain satisfactory compression ratio and higher peak signal-to-noise ratio (PSNR).The experimental results indicate that this method can achieve high performance.     

Structured-light Image Compression Based on Fractal Theory

The method of fractal image compression is introduced which is applied to compress the line structured light image. Based on the self similarity of the structured light image, we attain satisfactory compression ratio and higher peak signal to noise ratio (PSNR). The experimental results indicate that this method can achieve high performance.     

一种改进的分形图像压缩算法

为了提高分形图像压缩编码的速度,针对在基本分形图像压缩算法中值域块编码匹配搜索时需要对变换后的定义域块一一对应,导致编码时间较长的缺点,提出了一种基于菱形搜索算法的分形图像压缩编码新算法.菱形搜索算法是一种运动估计的快速搜索算法,主要过程是在所有的候选块中搜索当前块的最优匹配块.通过运用菱形搜索算法中的大小菱形模板进行匹配搜索,实验证明文中算法在提高编码速度和降低编码复杂度是有效的.     

分形学与图像压缩编码

介绍分形学的产生意义和作用，提出分形物体的数学模型，讨论分形技术应用于图像压缩编码中的迭代收缩变换算法。     

广义收敛的分形图像压缩编码

本文讨论了分形图像压缩的收敛性问题,给出了严格收敛和广义收敛的概念,提出了广义收敛的分形图像压缩编码方法(GC-FICC).实验证明,应用本文方法,可以在保证同样压缩比前提下,提高重建图像质量.     

基于混合编码的分形图像压缩方法研究

提出了一种基于混合编码的分形图像压缩方案,改进了分形编码与SP IHT算法,对提升小波变换后的最低频部分采用改进的分形编码,其他部分采用改进的SP IHT算法。试验结果表明,该方法在缩短了图像压缩时间的同时,明显减少了分形压缩恢复图像的方块效应。     

Low-Delay Parallel Architecture for Fractal Image Compression

This paper presents an efficient hardware architecture for implementing fractal image compression (FIC) algorithm aimed toward image compression with improved encoding speed. The proposed architecture follows the full-search-based FIC scheme. Parallel processing has been effectively used in the present work to achieve the goal of reducing the time complexity of the encoder. This architecture requires a total of \(2n+2\) clock cycles for executing the set of operations consisting of fetching the pixels, calculating the mean of range and domain blocks and doing their mapping, computing the error, and storing the fractal parameter in a memory with n number of pixels in the range block. Further, this architecture does not make use of any preprocessing operations as specified in literature and utilizes the benefits of isometric transformation without requiring additional cycles for every single matching operation. Effective application of isometric transformation has also led to memory reduction of nearly 67 %. Again, in the present work, the use of multipliers has been avoided to save the chip area, to reduce hardware complexity, and to enhance the encoding speed. The operation of transforming contracted domain block with a zero-mean domain block has facilitated relatively fast convergence at the decoder. PSNR above 30dB for a range block of size \(4\times 4\) has been achieved by the proposed architecture, which is comparable to that realizable by other architectures. The proposed design has been coded in Verilog HDL, has been implemented in Xilinx Virtex-5 FPGA, and operates at a clock frequency of 75.52 MHz.     

快速分形图象压缩编码

本文提出一种基于局部迭代函数系统（ＬＩＦＳ）的快速分形图象压缩编码、解码方法。实验表明，该法在恢复图象的信噪比为３０ｄＢ时，仍能达到２５倍的压缩倍数。     

基于分形与改进的SPIHT算法的图像压缩方法

为了避免分形编码所固有的方块效应,进一步提高图像编码的工作效率和重构图像的质量,对分形编码和小波零树编码进行优化组合,提出一种分形与改进的SPIHT算法相结合的图像压缩方法.基本方法是,对小波分解后的低频子带进行基于信息熵的快速分形编码,以减少编码时间;时包含图像细节边缘信息的高频子带进行改进的SPIHT编码,以舍去算法中对显著系数的排序扫描过程,减少算法的复杂度,同时提高重构图像的峰值信噪比.实验表明,相对于经典分形缟码和小波城内的分形编码,该方法在相同压缩比下,提高了编码效率和重构图像的质量,是一种高效快速的编码方法.     

一种新型图像分形压缩的改进算法

本文首先分析图像分形压缩技术中传统加速方法的性能缺陷,随后提出使用图像块的熵值来改进分形压缩性能的思想.在证明迭代函数系统不会改变图像块的熵值的结论基础上,本文给出了基于熵值的图像分形压缩基本方法及其扩展.实验结果说明该方法在压缩质量、压缩率、压缩时间等方面上都较传统方法有明显改善.     

Speeding Up Fractal Image Compression by Genetic Algorithms

The main problem with all fractal compression implementation is execution time. Algorithms can spend hours to compress a single image. Most of the major variants of the standard algorithm for speeding up computation time have led to a bad-quality or a lower compression ratio. For example, the Fishers [7] proposed classification pattern greatly accelerated the algorithm, but image quality was poor due to the search-space reduction imposed by the classification, which eleminates a lot of good solutions.By using genetic algorithms to address the problem, we optimize the domain blocks search. We explore all domain blocks present in the image but not in exhaustive way (like a standard algorithm) and without omitting any possible block (solution) as a classification pattern does. A genetic algorithm is the unique method for satisfying these constraints. And it is a way to do be a random search because the genetic one is directed by fitness selection, which produces optimal solutions.Our goal in this work is to use a genetic algorithm to solve the IFS inverse problem and to build a fractal compression algorithm based on the genetic optimization of a domain blocks search. we have also implemented standard Barnsley algorithm, the Y. Fisher based on classification, and the genetic compression algorithm with quadtree partitioning. A population of transformations was evolved for each range block, and the result is compared with the standard Barnsely algorithm and the Fisher algorithm = based classification.We deduced an optimal set of values for the best parameters combination, and we can also specify the best combination for each desired criteria: best compression ratio, best image quality, or quick compression process. By running many test images, we experimentally found the following set of optimal values of all the algorithm parameters that ensure compromise between execution time and solutions optimality: Population size = 100, Maximum generations = 20, Crossover rate = 0.7, Mutation rate = 0.1, RMS limit = 5, Decomposition error limit = 10, Flips and isometrics count = 8.In our proposed algorithm, results were much better than those obtained both vences and Rudomin [5] and Lankhorst [4] approaches.First online version published in May 2005     

基于树搜索的分形图像压缩编码

本文提出基于树搜索的分形图像压缩编码的新算法。该算法在恢复图像信噪比和主观质量较之全搜索算法略有损失的前提下，大大提高了分形图像编码的速度。     

分形插值图象放大和压缩编码

本文讨论了随机分形插值方法及其在图象放大和图象压缩编码中的应用。实验结果表明，用分形插值方法实现图象放大和压缩编码能获得良好的结果。     

基于平方加权质心特征的快速分形图像压缩编码

分形图像压缩利用自身图像具有的相似性,结合压缩仿射变换减少图像数据的冗余来实现图像数据的压缩,具有压缩比高、恢复简单的特点。然而,分形图像压缩编码也具有编码时间长、计算复杂的缺点。为了解决上述的缺点,提出了基于平方加权质心特征的快速分形图像压缩编码算法,利用平方加权质心特征可以将基本分形图像压缩编码过程中的全局搜索转化为局部搜索,限定搜索范围,减少码本数量,在巨大图像信息量传输和存储过程中,在一定程度上缩短了编码时间。将平方加权质心特征快速分形图像压缩编码算法和双交叉和算法、改进叉迹算法、规范五点和算法进行比较,仿真结果表明,所提算法在恢复质量可接受情况下,编码时间具有巨大优势。     

基于对称性与方差的快速分形人脸图像压缩

针对人脸具有对称性特点，提出了基于对称性与方差的快速分形人脸图像压缩算法。在编码时，对某值域块，将定义域块限定在与其对称的候选区内，如在其中未找到匹配定义域块，则再扩大候选区范围。对基于方差方法与本文算法在时间复杂性上的分析表明，即使在最坏情况下，利用本算法所需编码时间也仅为基于方差方法的1／2。通过在ORL和YALE人脸库中的实验表明，利用本文算法，在基本保持恢复图像质量的同时，平均编码时间仅为基于方差方法的1／3。实验还讨论了候选区、阈值与击中值域块个数、编码时间和峰值信噪比（PSNR）间的关系。     

分形插值图像压缩的一种快速解码算法

一维分形插值图像编码是用插值点数据构造分形曲线来拟合数字图像的灰度曲线从而实现压缩。其解码过程就是求用插值点数据构造的迭代函数系统(IFS)的吸引子,由于图像数据以及分形插值迭代规律的特殊性,使得随机迭代算法和通常的固定迭代算法并不适用。本文设计了快速且节省内存的解码算法,并进行了复杂度分析。同时,本文的算法作为分形插值方法的一部分,同样可以用在分形插值法的其他应用领域。     

一种基于K-均值聚类优化的快速分形图像压缩算法

提出了一种基于K-均值聚类的快速分形图像压缩算法,对搜索窗中的父块和子块,根据其方差的不同,用K-均值聚类方法分别对子块和父块进行聚类,子块只对同一类中的父块进行匹配,从而大大缩短了编码时间。实验结果表明,与经典分形压缩算法相比,本文算法编码速度可提高5倍;同基于方差的快速分形压缩算法相比,本文算法也有明显的优势。